no code implementations • 3 Apr 2024 • Ali Pesaranghader, Nikhil Verma, Manasa Bharadwaj
In this paper, we propose GPT-DETOX as a framework for prompt-based in-context learning for text detoxification using GPT-3. 5 Turbo.
no code implementations • 8 Aug 2023 • Ali Pesaranghader, Touqir Sajed
We initialize the weights of the entities with these embeddings to train our knowledge graph embedding (KGE) model.
2 code implementations • 1 Aug 2023 • Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Armin Toroghi, Anton Korikov, Ali Pesaranghader, Touqir Sajed, Manasa Bharadwaj, Borislav Mavrin, Scott Sanner
Experimental results show that Late Fusion contrastive learning for Neural RIR outperforms all other contrastive IR configurations, Neural IR, and sparse retrieval baselines, thus demonstrating the power of exploiting the two-level structure in Neural RIR approaches as well as the importance of preserving the nuance of individual review content via Late Fusion methods.
no code implementations • 14 Jun 2023 • Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang, Ali Pesaranghader, Manasa Bharadwaj, Scott Sanner
Text detoxification is a conditional text generation task aiming to remove offensive content from toxic text.
no code implementations • 25 Feb 2018 • Ahmad Pesaranghader, Ali Pesaranghader, Stan Matwin, Marina Sokolova
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports.
2 code implementations • 5 Oct 2017 • Ali Pesaranghader, Herna Viktor, Eric Paquet
Accordingly, concept drifts need to be detected, and handled, as soon as possible.
2 code implementations • 7 Sep 2017 • Ali Pesaranghader, Herna Viktor, Eric Paquet
In addition, a number of methods have been developed to detect concept drifts in these streams.